1. Field of the Invention
The present invention relates to a computer assisted image diagnosis apparatus that detects abnormal patterns within an image, based on medical image data, which is obtained by photographing a subject, and displays the abnormal pattern.
2. Description of the Related Art
Conventionally, image processes are administered to images, which are obtained by various image obtainment methods, to improve the image reading properties thereof. The image processes include gradation processes and frequency processes. Particularly in the field of medical images, such as radiation images having humans as the subjects thereof, it is necessary that experts such as physicians accurately diagnose the existence of disease and injuries in a patient, based on the obtained images. Therefore, image processes to improve the image reading properties of these images are crucial. For this reason, various methods have been proposed, in which image processes are employed so that tumor patterns can be diagnosed objectively, regardless of an observer's experience or image diagnosis capabilities.
Cancerous tumor patterns which are photographed as radiation images have a substantially rounded outline. At the same time, the tumor patterns are observed as regions having higher pixel values compared to their surroundings in the images. These tumor patterns are recognized as having a characteristic density value gradient. The characteristic density value gradient is that in which density values decrease from the edges toward the center of a semispherical region, in which equal density values spread in a concentric manner (hereinafter, referred to as “circular protrusion region”). These gradient lines concentrate toward the center of the rumor. By calculating the gradient of the density values as gradient vectors, tumor patterns can be detected from the degree of concentration of the gradient vectors (refer to J. Wei, Y. Ogihara, A. Shimizu, and H. Kobatake, “Characteristics Analysis of Convergence Index Filters”, Journal of the Society of Electronic Information Communications (D-II), Vol. J84-D-II No. 7, pp. 1289-1298, 2001, and J. Wei, Y. Ogihara, and H. Kobatake, “Convergence Index Filter for Detection of Lung Nodule Candidates”, Journal of the Society of Electronic Information Communications (D-II), Vol. J83-D-II No. 1, pp. 118-125, January 2000).
The size and shapes of filters for evaluating these degrees of concentration are designed to achieve detection capabilities which are unaffected by the size and/or shape of the tumor. Representative examples of such filters are iris filters and adaptive ring filters.
The iris filter outputs its maximum output value at the outlines of the circular protrusion regions, thereby enabling detection of candidate regions for abnormal patterns (refer to U.S. Pat. No. 5,761,334). On the other hand, the adaptive ring filter outputs extremely large values in the vicinity of the centers of the circular protrusion regions (refer to J. Wei et al., “Convergence Index Filter for Detection of Lung Nodule Candidates”, Journal of the Society of Electronic Information Communications (D-II), Vol. J83-D-II No. 1, pp. 118-125, January 2000).
A method for detecting abnormal pattern candidates is proposed in U.S. Pat. No. 5,732,121. In this method, candidate regions for abnormal patterns are detected by an iris filter or the like. Then, density histograms are derived for the interior of the candidate regions. Dispersion values, contrasts, angular moments and the like are calculated as characteristic amounts, based on the histograms. Evaluative function values are calculated from the characteristic amounts by a predetermined weighting function. Judgments are made regarding whether the candidate regions are malignant patterns, based on the calculated evaluative function values. In this manner, only malignant patterns are detected as abnormal pattern candidates.
Another method for detecting whether candidate regions are abnormal patterns is disclosed in U.S. Pat. No. 5,732,121 and Japanese Unexamined Patent Publication No. 2002-74325. In this method, normal patterns and abnormal patterns are classified into clusters according to characteristic amounts, which are based on previously obtained density histograms of candidate regions. When a candidate region appears in medical image data, a Mahalanobis distance Dm1 is measured from the candidate region to the pattern class that represents normal patterns. In addition, a Mahalonobis distance Dm2 is measured from the candidate region to the pattern class that represents abnormal patterns. Thereby, whether the candidate region corresponds to an abnormal pattern is detected.
Further, there are computer assisted diagnosis (CAD) systems that combine the aforementioned methods to reduce the burden on an observer, and to improve the quality of diagnosis. An example of the CAD system is disclosed in S. Furuya, J. Wei, Y. Ogihara, A. Shimizu, H. Kobatake, and S. Nawano, “Improvement of Detection Performance of Tumors on Mammograms by Sub-optimal Feature Set”, Society of Electronic Information Communications, Vol. 100 No. 434, pp. 93-100, 2001. The operations of the CAD system are as follows.    1) First, circular protrusion regions within original images are emphasized, by utilizing adaptive ring filters.    2) Next, extremely high points of the filter output values are extracted, and several (e.g., seven) high points are extracted as tumor candidate points.    3) Iris filters and a Snakes method are applied to the tumor candidate points, to extract tumor candidate regions. The Snakes method extracts outlines as optimal solutions, based on an energy minimization principle. The outputs of the iris filters are employed as Snakes energy. Details of the algorithm for candidate region determination according to the Snakes method are disclosed in H. Kobatake, M. Murakami, et al., “Computerized Detection of Malignant Tumors on Digital Mammograms”, IEEE Transactions on Medical Images, Vol. 18, No. 5, pp. 369-378, 1999.    4) Further, characteristic amounts are calculated from each of the determined candidate regions, and the candidate regions are classified into clusters of malignant tumor patterns ad benign tumor patterns, based on Mahalonobis distances.
In this manner, whether tumors are malignant or benign is judged, based on shape extraction results.
In addition, a CAD system has been proposed, wherein images, having visual characteristics similar to a medical image being observed, are searched for in a database of past cases (refer to M. Ginger, Z. Huo, C. Vybomy, L. Lan, I. Bonta, K. Horsch, R. Nishikawa, and I. Rosenborough, “Intelligent CAD Workstation for Breast Imaging Using Similarity to Known Lesions and Multiple Visual Prompt Aids”, Proceedings of SPIE, Medical Imaging 2002, February 2002, San Diego).
The aforementioned diagnosis assistance techniques make judgments based on abstracted characteristic amounts, such as the shapes and sizes of the candidate regions and the density contrasts thereof. Therefore, the judgments do not necessarily match with the visual perception of humans. For this reason, there are cases in which tumors that were judged as being similar are not similar, when viewed visually. For example, there are cases in which tumor images dissimilar to a tumor image in question are located from a database of past cases, when similar images are searched for.